Search Results for "graphs"

TAVIA is a desktop application for structured data analysis, visualization, reporting, and reproducible research workflows. It combines spreadsheet-style data handling with statistical tools, publication-ready graphs, confidence intervals, project files, encrypted storage, audit trails, workflow automation, and extensible plugins. The application includes CSV, TSV, XLSX, and SQLite import and export, customizable chart styles, confidence bands, error bars, report generation, generated analysis scripts, parameter sweeps, and a documented plugin API with a starter template. ...

VAERity is a free, open source tool to graphically explore the VAERS data set. It aims to eventually expand in scope to allow fast querying of arbitrary large datasets. It utilizes vaex and pandas as required to provide a balance of speed and query flexibility.

... - Implemented a 3d surface plot (GLSurface) based on OpenGL (ScienPlot v1.3.2 and above) - ColorMap surface, trisurface, Pie, Polar plots, and 3D height field, 3dBar, scatter plots (under developing), and more - Column by column plotting/calculation - LaTex commands enclosed by $ symbols can be used for the labels in Graph - Accept txt(Text) and csv(Comma separated values) formatted data - Save, copy, print Graph - Use spread sheets to display data - Textboard to organize the results - Graphs in a publishable quality - Source codes based on: Python Numpy Scipy Matplotlib WxPython Visvis etc. - Special functions - Drag and drop data files - Python console is back (since v1.3.3), capable of reusing column data - Debye and Guinier models for SANS / SAX data - More apps in our Web below

...With the back-end built using the numpy and scikit-learn libraries, as a toolkit, ExoPlanet couples fast and well tested algorithms, a UI designed over the Qt4 framework, and graphs rendered using Matplotlib to provide the user with a rich interface, rapid analytics and interactive visuals. ExoPlanet is designed to have a minimal learning curve, allowing researchers to focus on the applicative aspect of Machine Learning rather than their implementation details. It provides algorithms for unsupervised and supervised learning, which may be done with continuous or discrete labels. ...